predicting health and disease above and beyond traditional risk factors. To accomplish this objective we will develop statistical strategies to 1) determine which DMA sequence variations combine to improve the prediction of disease beyond the traditional risk factors in which subsets of a particular population, 2) validate the resultant multiplicity of models in the population of inference and 3) test the generalizability of the validated models in an independent population. The proposed study includes 1) capturing comprehensive DNA sequence variation in a network of genes that have been hypothesized to contribute to risk of cardiovascular disease and establish how their organization propagates constraints on genetic studies of a common chronic multifactorial disease, 2) developing non-traditional statistical methods for reducing the high dimensional network of genetic and environmental agents into subsets sufficient for predicting the network of intermediate traits that connect the genome with disease endpoints, 3) developing non-traditional statistical methods for reducing the high dimensional network of genetic and environmental agents into subsets sufficient for estimating the contribution of the network of DNA sequence variations to the prediction of disease endpoints in the population at large beyond the contribution of the network of intermediate biochemical and physiological traits and established risk factors and 4) estimating the relative roles of rare DNA sequence variations, common DNA sequence variations and rare combinations of common variations in explaining incident cases of CHD in the population at large. Co-investigators involved in this center program bring to this research endeavor expertise in genetics, statistics, molecular biology and medicine that is a consequence of 20+ years of collaborative research on complex common diseases of humans. Each of the co-investigators has decades of experience in teaching at the undergraduate and graduate level. One of the objectives of our renewal application will be to disseminate expert knowledge to the wider academic community about the most advanced measurement technologies and computation/statistical methods being used in carrying out a systems approach to genetic studies of common chronic diseases. To this end, we will implement a short course entitled "Genomic Approaches to Common Chronic Disease Research" to introduce systems biology research to advanced undergraduate and beginning graduate level students and offer a 10 week internship in the laboratory of one of the participating co-investigators.